As President Trump has announced varying tariffs over the last month, tech stock prices have dipped, with investors fearing broad impacts on different parts of the tech sector. Shares of NVIDIA, Taiwan Semiconductor Manufacturing Co (TSMC) and AMD have all wobbled, responding in part to news that Trump might implement a 25% tariff on semiconductors shipped to the U.S. The possible tariffs are said to be part of Trump's plan to try to get more semiconductor and AI-related manufacturing in the U.S. But industry insiders say that they're not yet changing any of their longterm approaches --and are mostly viewing the tariff threats as simply another input factor in a volatile industry whose prices are constantly shifting. "Tariffs are more of a blip as opposed to a strong headwind," says Scott Almassy, semiconductor lead at PwC. Nazar Khan, the COO and CTO of the data center company Terawulf, adds: "No one's really changing what they're doing because it's just too much guesswork." Trump's semiconductor tariffs could go into effect as early as April 2, he said.

A widely shared clip of Donald Trump Jr allegedly saying on his podcast that the US should have sent arms to Russia rather than Ukraine has been very likely generated using AI, audio forensics experts have told BBC Verify. Olga Robinson explains why the audio is suspect.

In November 2024, voters in the U.S. elected Donald Trump to a second, non-consecutive term as the nation's 47th President. Given U.S. prominence in the world, and the strong executive powers of the President, Trump and his administration will have a massive impact on everything from national security to the economy to the tenor of civic discourse--both inside and outside America. One area of impact being watched especially closely by policy experts: computer science. Technology powered by computer science, perhaps more so than at any other time in history, is expected to play a starring role in U.S. economic, cultural, and military strategies over the next four years. The world is in the midst of an unprecedented generative AI boom.

Moon exploration is undergoing a potentially transformative moment. Over the course of six weeks, three different lunar landers began a rocket-fueled space journey to learn more about Earth's nearest neighbor. All three landers are operated by private, and relatively newly-formed companies. That's a marked shift away from space exploration of the 20th century, which was dominated by state-backed, public institutions like NASA. If they complete their missions, these space upstarts could help pave the way for future planned human moon missions, and possibly, even a not-too distant lunar economy.

Gen. Peter Huntley, Lt. Gen. Jonathan Braga and Lt. Gen. Michael Conley urged Congress to take action to help U.S. troops advance. America is falling behind its adversaries when it comes to technological advancement, commanders of special operations forces told the House Armed Services Committee on Wednesday. Gen. Peter Huntley, Lt. Gen. Jonathan Braga and Lt. Gen. Michael Conley all voiced concerns about special ops being bogged down by bureaucracy when it comes to making critical technological upgrades. Gen. Huntley, who leads the Marine Forces Special Operations Command, told lawmakers on Capitol Hill that modernization is "moving very fast" across the globe, but America's ability to keep up with the pace is troublesome. Gen. Huntley spoke of the impact artificial intelligence has had on U.S. forces' ability to "sense the enemy before they sense us," an advantage America has enjoyed for decades.

A security camera in eastern Lebanon captured the moments a car was hit in a suspected Israeli drone attack. Lebanon's health ministry said one person was killed and another injured.

This paper presents the development and application of a Large Language Model Retrieval-Augmented Generation (LLM-RAG) system tailored for nanotechnology research. The system leverages the capabilities of a sophisticated language model to serve as an intelligent research assistant, enhancing the efficiency and comprehensiveness of literature reviews in the nanotechnology domain. Central to this LLM-RAG system is its advanced query backend retrieval mechanism, which integrates data from multiple reputable sources. The system retrieves relevant literature by utilizing Google Scholar's advanced search, and scraping open-access papers from Elsevier, Springer Nature, and ACS Publications. This multifaceted approach ensures a broad and diverse collection of up-to-date scholarly articles and papers. The proposed system demonstrates significant potential in aiding researchers by providing a streamlined, accurate, and exhaustive literature retrieval process, thereby accelerating research advancements in nanotechnology. The effectiveness of the LLM-RAG system is validated through rigorous testing, illustrating its capability to significantly reduce the time and effort required for comprehensive literature reviews, while maintaining high accuracy, query relevance and outperforming standard, publicly available LLMS.

The rapid proliferation of Large Language Models (LLMs) has raised pressing concerns regarding their trustworthiness, spanning issues of reliability, transparency, fairness, and ethical alignment. Despite the increasing adoption of LLMs across various domains, there remains a lack of consensus on how to operationalize trustworthiness in practice. This study bridges the gap between theoretical discussions and implementation by conducting a bibliometric mapping analysis of 2,006 publications from 2019 to 2025. Through co-authorship networks, keyword co-occurrence analysis, and thematic evolution tracking, we identify key research trends, influential authors, and prevailing definitions of LLM trustworthiness. Additionally, a systematic review of 68 core papers is conducted to examine conceptualizations of trust and their practical implications. Our findings reveal that trustworthiness in LLMs is often framed through existing organizational trust frameworks, emphasizing dimensions such as ability, benevolence, and integrity. However, a significant gap exists in translating these principles into concrete development strategies. To address this, we propose a structured mapping of 20 trust-enhancing techniques across the LLM lifecycle, including retrieval-augmented generation (RAG), explainability techniques, and post-training audits. By synthesizing bibliometric insights with practical strategies, this study contributes towards fostering more transparent, accountable, and ethically aligned LLMs, ensuring their responsible deployment in real-world applications.

Inflation prediction guides decisions on interest rates, investments, and wages, playing a key role in economic stability. Yet accurate forecasting is challenging due to dynamic factors and the layered structure of the Consumer Price Index, which organizes goods and services into multiple categories. We propose the Bi-directional Hierarchical Recurrent Neural Network (BiHRNN) model to address these challenges by leveraging the hierarchical structure to enable bidirectional information flow between levels. Informative constraints on the RNN parameters enhance predictive accuracy at all levels without the inefficiencies of a unified model. We validated BiHRNN on inflation datasets from the United States, Canada, and Norway by training, tuning hyperparameters, and experimenting with various loss functions. Our results demonstrate that BiHRNN significantly outperforms traditional RNN models, with its bidirectional architecture playing a pivotal role in achieving improved forecasting accuracy.

In recent years, there has been significant development of autonomous vehicle (AV) technologies. However, despite the notable achievements of some industry players, a strong and appealing body of evidence that demonstrate AVs are actually safe is lacky, which could foster public distrust in this technology and further compromise the entire development of this industry, as well as related social impacts. To improve the safety of AVs, several techniques are proposed that use synthetic data in virtual simulation. In particular, the highest risk data, known as corner cases (CCs), are the most valuable for developing and testing AV controls, as they can expose and improve the weaknesses of these autonomous systems. In this context, the present paper presents a systematic literature review aiming to comprehensively analyze methodologies for CC identifi cation and generation, also pointing out current gaps and further implications of synthetic data for AV safety and reliability. Based on a selection criteria, 110 studies were picked from an initial sample of 1673 papers. These selected paper were mapped into multiple categories to answer eight inter-linked research questions. It concludes with the recommendation of a more integrated approach focused on safe development among all stakeholders, with active collaboration between industry, academia and regulatory bodies.